Optimizing the Multi-Level Location-Assignment Problem in Queue Networks Using a Multi-Objective Optimization Approach

Using hubs in distribution networks is an efficient approach. In this paper, a model for the location-allocation problem is designed within the framework of the queuing network in which services have several levels, and customers must go through these levels to complete the service. The purpose of t...

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Bibliographic Details
Published in:Foundations of computing and decision sciences Vol. 47; no. 2; pp. 177 - 192
Main Authors: Syah, Rahmad, Elveny, Marischa, Soerjati, Enni, Guerrero, John William Grimaldo, Jowad, Rawya Read, Suksatan, Wanich, Aravindhan, Surendar, Voronkova, Olga Yuryevna, Mavaluru, Dinesh
Format: Journal Article
Language:English
Published: Poznan Sciendo 01.06.2022
De Gruyter Brill Sp. z o.o., Paradigm Publishing Services
Subjects:
Hub
Integer programming
Location-Assignment
Multi-Objective Optimization
Multilevel Services
Queue Theory
Reinforced Epsilon Constraint Method
ISSN:2300-3405, 0867-6356, 2300-3405
Online Access:Get full text
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Summary:Using hubs in distribution networks is an efficient approach. In this paper, a model for the location-allocation problem is designed within the framework of the queuing network in which services have several levels, and customers must go through these levels to complete the service. The purpose of the model is to locate an appropriate number of facilities among potential locations and allocate customers. The model is presented as a multi-objective nonlinear mixed-integer programming model. The objective functions include the summation of the customer and the waiting time in the system and the waiting time in the system and minimizing the maximum possibility of unemployment in the facility. To solve the model, the technique of accurate solution of the epsilon constraint method is used for multi-objective optimization, and Pareto solutions of the problem will be calculated. Moreover, the sensitivity analysis of the problem is performed, and the results demonstrate sensitivity to customer demand rate. Based on the results obtained, it can be concluded that the proposed model is able to greatly summate the customer and the waiting time in the system and reduce the maximum probability of unemployment at several levels of all facilities. The model can also be further developed by choosing vehicles for each customer.
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ISSN:2300-3405
0867-6356
2300-3405
DOI:10.2478/fcds-2022-0010